HR Analytics Dashboard — Employee Attrition Analysis

End-to-end HR analytics project analyzing employee attrition patterns using the IBM HR Analytics dataset across Excel, SQL, Python, and Power BI.

Dashboard Preview

Tech Stack

• Excel — Data cleaning, pivot tables, KPI calculations
• PostgreSQL — 10 analytical SQL queries
• Python — EDA, correlation analysis, visualizations
• Power BI — 5-page interactive dashboard with DAX

Dataset

IBM HR Analytics Employee Attrition Dataset

• 1,470 employees · 35 features · Available on Kaggle

Key Business Insights

• Overall attrition rate: 16.1% (above 10% industry benchmark)
• Sales department has highest attrition at 20.6%
• Overtime workers are 3x more likely to leave (30.5% vs 10.4%)
• Employees who left earned ₹2,046 less on average than those who stayed
• Single employees leave at 25.5% — highest among marital status groups

Project Structure

hr-analytics/ ├── data/ │ └── hr_employee_cleaned.csv ├── sql/ │ └── queries.sql ├── python/ │ └── eda.py ├── outputs/ │ └── (8 EDA charts) └── HR_Analytics_Dashboard.pbix

Dashboard Pages

1. Executive Dashboard — 6 KPI cards, attrition split, dept & role charts
2. Attrition Deep Dive — Age, marital status, travel, education analysis
3. Salary & Satisfaction — Income gap, job satisfaction, work life balance
4. Overtime & Experience — Overtime impact, tenure, job level analysis
5. Demographics — Gender, age distribution, education field, income band

How to Run

1. Download IBM HR dataset from Kaggle
2. Open hr_employee_cleaned.csv in Excel for data cleaning
3. Load to PostgreSQL and run sql/queries.sql
4. Run python python/eda.py for EDA charts
5. Open HR_Analytics_Dashboard.pbix in Power BI Desktop